He Sheng

He Sheng
Harvard Medical School | HMS · Department of Radiology

Doctor of Philosophy

About

37
Publications
27,547
Reads
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851
Citations
Citations since 2016
29 Research Items
790 Citations
2016201720182019202020212022050100150
2016201720182019202020212022050100150
2016201720182019202020212022050100150
2016201720182019202020212022050100150
Additional affiliations
January 2013 - present
University of Groningen
Position
  • PhD Student
January 2009 - January 2012
Northwestern Polytechnical University
Position
  • Master's Student

Publications

Publications (37)
Article
Saliency detection aims at quantitatively predicting attended locations in an image. It may mimic the selection mechanism of the human vision system, which processes a small subset of a massive amount of visual input while the redundant information is ignored. Motivated by the biological evidence that the receptive fields of simple-cells in V1 of t...
Article
In this paper, we propose a novel junction detection method in handwritten images, which uses the stroke-length distribution in every direction around a reference point inside the ink of texts. Our proposed junction detection method is simple and efficient, and yields a junction feature in a natural manner, which can be considered as a local descri...
Preprint
Full-text available
Most deep learning models for temporal regression directly output the estimation based on single input images, ignoring the relationships between different images. In this paper, we propose deep relation learning for regression, aiming to learn different relations between a pair of input images. Four non-linear relations are considered: "cumulative...
Article
Most deep learning models for temporal regression directly output the estimation based on single input images, ignoring the relationships between different images. In this paper, we propose deep relation learning for regression, aiming to learn different relations between a pair of input images. Four non-linear relations are considered: "cumulative...
Preprint
Full-text available
Deep learning can provide rapid brain age estimation based on brain magnetic resonance imaging (MRI). However, most studies use one neural network to extract the global information from the whole input image, ignoring the local fine-grained details. In this paper, we propose a global-local transformer, which consists of a global-pathway to extract...
Article
Deep learning can provide rapid brain age estimation based on brain magnetic resonance imaging (MRI). However, most studies use one neural network to extract the global information from the whole input image, ignoring the local fine-grained details. In this paper, we propose a global-local transformer, which consists of a global-pathway to extract...
Article
In the era of antimicrobial resistance, the prevalence of multidrug-resistant microorganisms that resist conventional antibiotic treatment has steadily increased. Thus, it is now unquestionable that infectious diseases are significant global burdens that urgently require innovative treatment strategies. Emerging studies have demonstrated that artif...
Article
Document binarization is a key step in most document analysis tasks. However, historical-document images usually suffer from various degradations, making this a very challenging processing stage. The performance of document image binarization has improved dramatically in recent years by the use of Convolutional Neural Networks (CNNs). In this paper...
Preprint
Full-text available
This paper presents an end-to-end neural network system to identify writers through handwritten word images, which jointly integrates global-context information and a sequence of local fragment-based features. The global-context information is extracted from the tail of the neural network by a global average pooling step. The sequence of local and...
Article
This paper presents an end-to-end neural network system to identify writers through handwritten word images, which jointly integrates global-context information and a sequence of local fragment-based features. The global-context information is extracted from the tail of the neural network by a global average pooling step. The sequence of local and...
Article
Brain age estimated by machine learning from T1-weighted magnetic resonance images (T1w MRIs) can reveal how brain disorders alter brain aging and can help in the early detection of such disorders. A fundamental step is to build an accurate age estimator from healthy brain MRIs. We focus on this step, and propose a framework to improve the accuracy...
Article
Full-text available
For performing multi-class classification, deep neural networks almost always employ a One-vs-All (OvA) classification scheme with as many output units as there are classes in a dataset. The problem of this approach is that each output unit requires a complex decision boundary to separate examples from one class from all other examples. In this pap...
Conference Paper
Brain age prediction based on children's brain MRI is an important biomarker for brain health and brain development analysis. In this paper, we consider the 3D brain MRI volume as a sequence of 2D images and propose a new framework using the recurrent neural network for brain age estimation. The proposed method is named as 2D-ResNet18+Long short-te...
Article
Writer identification 1 based on a small amount of text is a challenging problem. In this paper, we propose a new benchmark study for writer identification based on word or text block images which approximately contain one word. In order to extract powerful features on these word images, a deep neural network, named FragNet, is proposed. The FragNe...
Preprint
Full-text available
Writer identification based on a small amount of text is a challenging problem. In this paper, we propose a new benchmark study for writer identification based on word or text block images which approximately contain one word. In order to extract powerful features on these word images, a deep neural network, named FragNet, is proposed. The FragNet...
Preprint
Brain age prediction based on children's brain MRI is an important biomarker for brain health and brain development analysis. In this paper, we consider the 3D brain MRI volume as a sequence of 2D images and propose a new framework using the recurrent neural network for brain age estimation. The proposed method is named as 2D-ResNet18+Long short-te...
Preprint
This paper presents a novel iterative deep learning framework and apply it for document enhancement and binarization. Unlike the traditional methods which predict the binary label of each pixel on the input image, we train the neural network to learn the degradations in document images and produce the uniform images of the degraded input images, wh...
Article
There are two types of information in each handwritten word image: explicit information which can be easily read or derived directly, such as lexical content or word length, and implicit attributes such as the author's identity. Whether features learned by a neural network for one task can be used for another task remains an open question. In this...
Preprint
Full-text available
There are two types of information in each handwritten word image: explicit information which can be easily read or derived directly, such as lexical content or word length, and implicit attributes such as the author's identity. Whether features learned by a neural network for one task can be used for another task remains an open question. In this...
Preprint
Full-text available
Recognition of Off-line Chinese characters is still a challenging problem, especially in historical documents, not only in the number of classes extremely large in comparison to contemporary image retrieval methods, but also new unseen classes can be expected under open learning conditions (even for CNN). Chinese character recognition with zero or...
Conference Paper
Full-text available
This paper describes the ICFHR 2018 Competition on Multi-script Writer Identification with details on the competition tasks, databases employed, submitted systems, evaluation protocol and the reported results. The competition was aimed at exploring the traditional writer identification problem in a more challenging scenario of a multi-script enviro...
Conference Paper
Full-text available
To understand the historical context of an ancient manuscript, scholars rely on the prior knowledge of writer and date of that document. In this paper, we study the Dead Sea Scrolls, a collection of ancient manuscripts with immense historical, religious, and linguistic significance, which was discovered in the mid-20th century near the Dead Sea. Mo...
Article
Manuscript dating is an essential part of historical scholarship. This paper proposes a framework for image-based historical manuscript dating based on handwritten pattern analysis in scanned historical manuscript images. We first use a singular structural feature to extract the mid-level handwritten patterns in historical document images and then...
Article
Full-text available
It is of essential importance for historians to know the date and place of origin of the documents they study. It would be a huge advancement for historical scholars if it would be possible to automatically estimate the geographical and temporal provenance of a handwritten document by inferring them from the handwriting style of such a document. We...
Article
The explosive growth of digital video data renders a profound challenge to succinct, informative, and human-centric representations of video contents. This quickly-evolving research topic is typically called 'video abstraction'. We are motivated by the facts that the human brain is the end-evaluator of multimedia content and that the brain's respon...
Conference Paper
Full-text available
This paper presents a method for extracting rotation-invariant features from images of handwriting samples that can be used to perform writer identification. The proposed features are based on the Hinge feature [1], but incorporating the derivative between several points along the ink contours. Finally, we concatenate the proposed features into one...
Conference Paper
Full-text available
Image saliency detection provides a powerful tool for predicting where human tends to look at in an image, which has been a long attempt for the computer vision community. In this paper, we propose a biologically-inspired model for computing image saliency. At first, a set of basis functions that accords with visual responses to natural stimuli is...
Article
Computer vision community has long attempted to automatically detect locations in the image that are able to capture attentions of users. In recent years, more and more researchers have proposed to address this problem from the perspective of simulating human visual attention mechanisms. In this paper, we study modeling visual attention in frequenc...

Questions

Question (1)
Question
Hi everyone. I want to fill a closed contour in C++. But I donot know how to fill it efficiently and correctly. Like the function imfill in matlab.

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Projects

Projects (5)
Project
The Monk project is a project funded over the years, since 2005, by several funding agencies and is located at the University of Groningen, The Netherlands. Its goal is to provide e-Science tools for users in the humanities and in pattern recognition, with word search in handwritten historical collection as the main function. The system is trained by scholars and has over 400 documents, 80k+ scans and 777k labeled characters and words harvested (end 2016). Methods are diverse, with as the common approach the iteration between recognition (separability) and ranking (prototypicality) using methods that are most suited for either of these functions. Autonomous 24/7 training was switched on in 2009, using event detection, job queuing and HPC computing. The system started on the 10 PB Target platform in Groningen.
Archived project
This is a project granted by the Dutch Organisation for Scientific Research NWO in 2012. Its goal is to deliver a web-based system for dating medieval charters by uploading them to a server which uses image processing and pattern recognition, together with a reference data set, a paleographic scale, to produce an estimate of the when, where (and possibly who) of the manuscript sample.